Comfort-based garment management

ABSTRACT

Comfort-based garment management can include establishing a sensor output threshold for a garment based on comfort data, receiving sensor output data from a number of sensors integrated with the garment, comparing the sensor output data with the sensor output threshold, and sending a notification to a party associated with the garment in response to the sensor output data crossing the sensor output threshold.

BACKGROUND

Sensors can be included in garments. For example, sensors can beintegrated into garments and output measurements. The measurements caninclude measurements of forces experienced by the garment and the userof the garment.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example of an environment for comfort-basedgarment management according to the present disclosure.

FIG. 2 illustrates a diagram of an example of a system for comfort-basedgarment management according to the present disclosure

FIG. 3 illustrates a diagram of an example of a computing deviceaccording to the present disclosure

FIG. 4 is a flow chart of an example of a method for comfort-basedgarment management according to the present disclosure

DETAILED DESCRIPTION

Garment supply chains can provide garments from manufacturers toconsumers. Conventional methods of garment management within a supplychain rely on modeling supply and demand. Predicting demand and managingsupply in the garment industry can be particularly challenging. Due tothe ever-changing landscape of garment preferences, the lead-time gap inthe garment industry must be very brief compared with other commodities.That is, if a garment manufacturer takes too long in manufacturing thegarment, the garment may be become undesirable to the consumer base.This is often at odds with providing a high quality garment that fitseach consumer's individual needs as such an endeavor often requiresincreased lead-time. Current supply and demand models focus on managinga garment based on economic factors and consumer purchasing habits.However, these models are incomplete and lack important information notonly in predicting/managing demand of individual consumers forparticular garments, but are also unable to provide specific feedback tomanufacturers, retailers, and/or consumers as to what is making theirgarments appealing to individual consumers and how the garment and/orsimilar garments might be improved to appeal to a consumer.

In contrast, the embodiments of the present disclosure describe acomfort-based garment management system for managing a garment withrespect to the manufacturer, the retailer, and the consumer. Managing agarment within a supply chain can be dependent on understandingsubjective and individualistic determinations of the garment'sdesirability. These determinations can include determinations of agarment's comfort.

Individuals' perceptions of garment comfort can influence their behaviorwith regard to purchasing the garment, replacing the garment, and buyingsimilar garments. Understanding and predicting these perceptions canprovide a retailer and/or manufacturer guidance with respect to managinga garment within a supply chain. Furthermore, it can provide anopportunity for a dialog with the individual consumers regarding theirmanagement of the garment and related purchasing decisions.

Sensors can be used to measure physical quantities. Sensors can beincorporated into clothing. Associating sensor measurements (e.g.,sensor outputs) with subjective determinations, such as comfort, canhelp to quantify what comfort means with respect to a consumer (e.g., anindividual consumer, a group of consumers, a type of consumer, etc.) ofa garment.

FIG. 1 illustrates an example of an environment 100 for comfort-basedgarment management according to the present disclosure. The environment100 is shown to include a system 102 for comfort-based garmentmanagement, computing devices 104-1, . . . , 104-N (e.g., servers), aconsumer 106, a retailer 108, a manufacturer 110, a data store 112, alink 114, and a cloud based computing system 116. The data store 112 canbe a single data store or a distributed data store and can be analogousto the data store discussed with respect to FIG. 2. The data store canbe all or partially based in the cloud 116.

A given computing device 104-1, . . . , 104-N can include a web server,a mainframe, a cloud server, an application server, a client server,and/or a data server, among other types of servers. A consumer 106, aretailer 108, and a manufacturer 110 can include any device associatedwith the consumer 106 of a garment, the retailer 108 of a garment, andthe manufacturer 110 of the garment. The devices can include anysuitable computing devices with browsers or other applications toreceive data, communicate data and/or process data. Furthermore, theconsumer 106, retailer 108, and manufacturer 110 can each include anumber of associated garments having a number of sensors capable ofmeasuring physical attributes and communicating data. For example, thenumber of sensors can be integrated into the garment. These sensors canbe capable of transmitting their measurements independently of anyancillary device (e.g., a separate computing device capable ofreceiving, processing, and/or receiving data including sensormeasurements) and/or capable of transmitting their measurements to anancillary device of the consumer 106, retailer 108, and/or manufacturer110 for further processing and/or transmittal over the link 114.

Link 114 (e.g., a network) represents a cable, wireless, fiber optic,and/or remote connection via a telecommunication link, an infrared link,a radio frequency link, and/or other connectors or systems that provideelectronic communication. That is, the link 114 can, for example,include a link to an intranet, the Internet, or a combination of both,among other communication interfaces. The link 114 can also includeintermediate network devices such as proxy servers, routers, switches,load balancers, and the like.

As illustrated in FIG. 1, a cloud system 116 can include a public cloudsystem, a private cloud system, and/or a hybrid cloud system. Forexample, an environment (e.g., IT environment) including a public cloudsystem and a private cloud system can include a hybrid environmentand/or a hybrid cloud system. A public cloud system can include aservice provider that makes resources (e.g., applications, storage,and/or data), available to the public over the Internet. A public cloudsystem can be free or offered for a fee, for example.

A private cloud system can include computing architecture that provideshosted services to a limited number of people behind a firewall. Forexample, a private cloud can include an Enterprise Resource Planning(ERP) system, a number of databases, and virtualization (e.g., virtualmachines). For instance, a private cloud system can include a computingarchitecture that provides hosted services to a limited number of aplurality of nodes (e.g., computers) behind a firewall. The ERP, forexample, can integrate internal and external management informationacross an entire supply chain, enterprise, and/or organization. A numberof databases can include an event database, event archive, configurationmanagement database (CMDB), and/or a performance metric database, forexample. Virtualization, for example, can include the creation of anumber of virtual resources that are allocated from physical resourcesbut not directly limited by the capabilities of particular physicalresources. Examples of virtualized resources include hardware platforms,operating systems, storage devices, and/or network resources, amongothers. For example, a virtual storage device can provide up to aparticular capacity of storage that is physically provided by one, lessthan one, or more than one physical storage device depending on theamount of storage space allocated to the virtual storage device andtherefore not directly limited by the capabilities of any particulardevice(s). The public cloud system and the private cloud system can bebound together, for example, through the application in the public cloudsystem and the ERP in the private cloud system.

A hybrid cloud, for example, can include a mix of traditional serversystems, private cloud systems, public cloud systems, and/or dynamiccloud services. For instance, a hybrid cloud can involveinterdependencies between physically and logically separated servicesconsisting of multiple systems. A hybrid cloud, for example, can includea number of clouds (e.g., two clouds) that can remain unique entitiesbut can be bound together.

The system 102 for comfort-based garment management can representdifferent combinations of hardware or hardware and instructions forcomfort-based garment management. The system 102 for comfort-basedgarment management can include a computing device, for instance,computing device 350 as discussed with respect to FIG. 3. The system 102can include engines analogous to engines described herein with respectto FIG. 2. For example, the system 102 can include a baseline engine, acomfort engine, a comparison engine, and a notification engine, asdescribed herein with respect to FIG. 2. A portion or all of the system102 for comfort-based garment management can be implemented usingresources of the cloud 116.

In FIG. 1, the consumer 106, retailer 108, and/or manufacturer 110 cantransmit data to computing devices 104-1, . . . , 104-N in the cloudsystem 116. This data can include any data (e.g., retail metrics, sensoroutputs, specifications, consumer dimensions, consumer identities, etc.)useful in comfort-based garment management. The data in the cloud system116 can then be shared, analyzed, and utilized in managing a garment atany stage of the supply chain. This data can also be utilized in thesystem 102 for comfort-based garment management.

FIG. 2 illustrates a diagram of an example of a system 230 forcomfort-based garment management according to the present disclosure.The system 230 can include a data store 234, a comfort-based garmentmanagement system 232, and/or a number of engines (e.g., the baselineengine 236, the comfort engine 238, the comparison engine 240, and thenotification engine 242). The comfort-based garment management system230 can be in communication with the data store 234 via a communicationlink, and can include, manage, and/or employ the number of engines(e.g., the baseline engine 236, the comfort engine 238, the comparisonengine 240, and the notification engine 242) to perform variousfunctions. The comfort-based garment management system 230 can includeadditional or fewer engines than illustrated to perform the variousfunctions described herein.

The number of engines (e.g., the baseline engine 236, the comfort engine238, the comparison engine 240, and the notification engine 242) caninclude hardware or a combination of hardware and programming to performa number of functions described herein (e.g., establishing a sensoroutput threshold for a garment based on comfort data, receiving sensoroutput data from a number of sensors integrated with the garment,comparing the sensor output data with the sensor output threshold,sending a notification to a party associated with the garment inresponse to the sensor output data crossing the sensor output threshold,etc.). The programming can include program instructions (e.g., software,firmware, etc.) stored in a memory resource (e.g., computer readablemedium, machine readable medium, etc.) as well as hard-wired program(e.g., logic).

The baseline engine 236 can include hardware and/or a combination ofhardware and programming to establish a sensor output threshold for agarment based on comfort data. The garment of system 230 can be anygarment (e.g., any item of clothing, accessory, headwear, hand wear,footwear, jewelry, protective ware, athletic equipment, worn fiberand/or textile, etc.) or number of garments (e.g., an outfit, relatedgarments, etc.).

Examples herein can include sensors integrated with the garment. Thesensors can be integrated into the garments. In example embodiments,sensors can be incorporated within the garment (e.g., woven into thefabric, implanted into the material, etc.). Alternatively oradditionally, the sensors can be external integrated. For example, thesensors can be applied to the inside and/or outside surface of thegarment. Examples can include washable sensors that may be washed alongwith the garments without damaging the sensors and/or the garment.

The sensors can continuously and/or periodically sense physicalquantities and convert them to data (e.g., sensor outputs). Garmentsensors can include any instrument capable of measuring a physicalquantity and/or converting the measurement to a signal. For example,garment sensors can include, individually or in combination, any numberof instruments that are capable of sensing forces exerted on a garment,sensing forces exerted on a wearer of the garment, sensing properties ofthe garment, and/or sensing properties of the wearer of the garment.Examples herein can include heat sensors, pressure sensors,accelerometers, gyroscopes, temperature sensors, footfall sensors, flexsensors, thickness sensors, chemical sensors, tensile load sensors,compressive load sensors, light sensors, and perspiration sensors, amongothers.

The comfort data utilized by baseline engine 236 can include datarelated to the sensor outputs of a number of sensors integrated with agarment. The comfort data can be specific to a particularconsumer/garment or generic to groups of consumers/garments. Forexample, the comfort data can include consumer dimensions (e.g., height,weight, bust, distance around waist, hip width, inseam, shoulder towrist length, armpit to wrist length, distance around shoulders,distance around head, distance around neck, foot length, foot width,sizes, other measurements, etc.). These dimensions, which can besupplied by an individual consumer, the retailer, and/or themanufacturer, can be based on dimensions unique to the particularconsumer purchasing and/or wearing a particular garment or consideringpurchasing that garment. In this manner, the comfort data is unique andspecific to a particular consumer. Alternatively, the consumerdimensions can be generic dimensions associated with a group ofconsumers. For example, a retailer and/or manufacturer can develop anaverage set of dimensions associated with clientele consumers and thesedimensions can be utilized as comfort data.

The consumer dimensions can be input by the consumer, retailer, and/ormanufacturer at any point in the supply chain of a garment being managed(e.g., before the sale of a garment, during a garment fitting, duringthe sale of a garment, after the sale of a garment, etc.).Alternatively, the dimensions can be retrieved from records (e.g.,electronic records stored in the cloud and/or on devices of theconsumer, retailer, or manufacturer). These records can be based onhistorical dimensions of the consumer that were input with respect toprevious garment purchases.

The comfort data can additionally include expected (e.g., predictedbased on a model, predicted based on prior knowledge, etc.) sensoroutput values. That is, the comfort data can include a number ofexpected sensor output values of a number of sensors integrated with agarment being managed. The expected sensor output values can be expectedsensor values from sensors integrated into the garment being managedthat are predicted to coincide with a comfortable fit for the consumer(e.g., individual consumer, group consumers, etc.). These expectedsensor output values can be received from a consumer, a retailer, and/ora manufacturer and/or can be derived from other information. Forexample, the consumer dimensions can be analyzed with the use of a modelto generate the expected sensor output values from sensors integratedinto the garment being managed that are predicted to coincide with acomfortable fit for the consumer having the consumer dimensions.

Furthermore, the expected sensor output values predicted to coincidewith a comfortable fit for the consumer can be based on historicalsensor output values. For example, if a particular consumer haspreviously provided sensor output values from sensors integrated into agarment (e.g., an identical garment, a similar garment, a relatedgarment, etc.) which coincided with a comfortable fit for that consumerwith respect to that garment, then the previously provided sensor outputvalues can be utilized as historical sensor output values in predictingexpected sensor values from sensors integrated into the garment beingmanaged that are predicted to coincide with a comfortable fit for thatconsumer. The historical sensor output values can be utilized in anunmodified state as the expected sensor output values and/or modifiedfor greater accuracy with respect to the garment being managed.

The use of historical sensor output values can also be applied moregenerically. That is, the historical sensor output values from oneconsumer or group of consumers can be utilized in an unmodified and/ormodified state in predicting expected sensor values for other consumers.For example, if consumer A and consumer B have similar consumerdimensions and historical sensor output values exist for consumer A fora particular garment, then the historical sensor output values forconsumer A may be utilized with respect to consumer B in predictingexpected sensor values from sensors integrated into a same or similargarment being managed.

In another example, if the manufacturer of a garment being managed hasdetermined (e.g., based on knowledge, research, computer models, etc.)that certain sensor output values commonly correspond to a comfortablefit of the garment for its clientele, the manufacturer may designatethese output values as expected sensor output values associated with acomfortable fit for that garment for an individual consumer. Likewise, aretailer may generate similar designations of expected sensor outputvalues for a garment being managed.

The comfort data can additionally or alternatively include baselinesensor output values. A baseline sensor output value can include anumber of actual outputs from a number sensors integrated with a garmentbeing managed. For example, a baseline sensor output value can be asensor output value from a sensor integrated with the garment beingmanaged collected while the consumer is wearing the garment. Thisconcept is equally applicable when the consumer is wearing an identicalgarment, a similar garment, and/or a related garment to the garmentbeing managed. By collecting the baseline sensor output values while theconsumer is wearing the garment, the consumer can simultaneouslyidentify the sensor output values coinciding with his subjectivedetermination of a comfortable fit of the garment. That is, a subjectivedetermination of a comfortable fit by a particular consumer can bequantified by the coinciding sensor output values. The baseline sensoroutput values can be established at any time in the garment supplychain. For example, the baseline sensor output values can be establishedduring manufacture, at the retailer, after purchase of the garment,and/or at any other time that the consumer is available to try on thegarment. In some examples, the consumer of a garment being managed cantry on the garment at a retailer. During this period the sensor outputvalues from the garment being managed can be collected. The consumer canindicate to, for example, the retailer, while wearing the garment,whether the garment has a comfortable fit. Accordingly, upon eachdetermination by the consumer that a garment has a comfortable fit thecoinciding sensor output values of the sensors integrated with thatgarment are collected. These collect sensor output values can bebaseline sensor output values. Additionally, a number of these sensoroutput values can be combined in order to generate an average baselinesensor output value. The baseline sensor output values can, for example,be uploaded to the cloud by the retailer and or the consumer. In someexamples, the baseline sensor output values may be uploaded to aconsumer's electronic device to be managed and uploaded to the cloud byan application.

Establishing a sensor output threshold based on the above describedcomfort data can include characterizing an acceptable range of sensoroutput values from sensors integrated with a garment being managed thatwill generally coincide a comfortable garment fit for the consumer ofthe garment. For example, establishing the sensor output thresholds caninclude defining threshold sensor output values that represent a minimumand/or a maximum sensor output value from a garment being managed thatwould coincide with a comfortable fit of the garment with respect to theconsumer based on what the comfort data reveals about the preferences ofthe consumer. As an example, the minimum and maximum sensor outputvalues can be based on a baseline sensor output value coinciding with acomfortable fit determination of the consumer for the garment beingmanaged. Additionally, the minimum and maximum sensor output values canbe based on an expected sensor output value of sensors integrated withthe garment being managed that is predicted to coincide with acomfortable fit for the consumer. Sensor output values from the sensorsintegrated into the garment being managed that fall outside of thesethresholds can represent sensor output values corresponding with anuncomfortable or undesirable fit of the garment.

Establishing the sensor output thresholds as described herein caninclude establishing thresholds with respect to the sensor output valuesof each individual sensor integrated with a garment being managed, withrespect to the sensor output values of groups of sensors integrated witha garment being managed, and/or with respect to comfort scoresassociated with the sensor output of sensors integrated with a garmentbeing managed. A comfort score can be a score derived from anycombination of the sensor output values of a number of sensorsintegrated with a garment being managed. For example, the comfort scorecan be a score based on applying a mathematical algorithm to the sensoroutput data of a number of sensors integrated with a garment beingmanaged, wherein the sensor outputs are assigned predetermined weights.For example, if pressure and heat sensor output values are betterindicators of comfort in the garment being managed than other sensoroutput values of the number of sensors integrated with a garment beingmanaged, then the heat and pressure sensor output values can be weightedmore heavily in generating the comfort score.

The comfort engine 238 can include hardware and/or a combination ofhardware and programming to receive sensor output data from a number ofsensors integrated with the garment being managed. The sensor outputdata received utilizing the comfort engine can be sensor output datagenerated subsequent to establishing the sensor output threshold for thegarment based on comfort data.

The number of sensors of a garment can generate sensor output data(e.g., sensor output values, cumulative scores associated with sensoroutput values of a number of sensors of a garment, etc.) continuously orperiodically. For example, the number of sensors of a garment beingmanaged can generate sensor output data continuously upon beingactivated (e.g., upon initially energizing the sensors, upon activatingthe sensors through an electronic command, etc.). Alternatively thesensors of a garment can generate output periodically in response to atrigger (e.g., in response to stimuli, in response to periodicenergizing of the sensors, in response to periodic electronic commands,etc.).

Receiving the sensor output data can also occur continuously orperiodically. Receiving the sensor output data can include receiving thesensor output data in the cloud. For example, the sensor output data canbe uploaded to the cloud from the garment sensors directly, and or fromthe garment sensors via an electronic device receiving the sensor outputdata continuously. Alternatively, the garment sensor output data can bereceived periodically. For example, the sensor output data can beuploaded to the cloud in response to a trigger (e.g., an electroniccommand, a scheduled upload, a query by the comfort-based garmentmanagement system 232, automatically by the presence of a dataconnection, submission of the data by the consumer to the retailer ofthe garment, submission of the data by the consumer to the manufacturerof the garment, etc.). Receiving the sensor output data from a number ofsensors integrated with the garment being managed can occur without theconsumer being aware that the sensor output data is being transmitted.

The comparison engine 240 can include hardware and/or a combination ofhardware and programming to compare the sensor output data from thenumber of sensors integrated with a garment being managed with theestablished sensor output threshold. Comparing the sensor output datawith the sensor output threshold can include comparing the sensor outputdata with the sensor output threshold in substantially real-time as thesensor output data is received from the sensors integrated with thegarment. Alternatively, the sensor output data can be compared to thesensor output threshold upon retrieving one or both of the sensor outputdata and the sensor output threshold from the cloud. For example,comparing the sensor output data with the sensor output threshold caninclude determining whether the sensor output data is outside of theestablished thresholds and, if so by how much.

The notification engine 242 can include hardware and/or a combination ofhardware and programming to send a notification to a party associatedwith the garment in response to the sensor output data from the numberof sensors integrated with a garment crossing the sensor outputthreshold. The notification can include any type of communication (e.g.,an email, a text message, an alert, a signal, a telephone message, apaper mailing, etc.).

The contents of the notification can vary greatly and can, for example,depend on the intended recipient. For example, if the intended recipientof the notification is the consumer that purchased the garment beingmanaged then the notification can include information related to thegarment he purchased. As an example, the notification could be anotification that the garment is reaching the end of a pre-definedlifecycle. In such examples, the manufacturer and/or retailer can definea lifecycle of a garment as coinciding with its comfort level for theconsumer as indicated by the sensor out data from the number of sensorsintegrated with the garment. For example, the manufacturer and/orretailer may have determined that once the sensor output data for agarment being managed has crossed the established sensor outputthreshold for the garment, the garment has reached or is nearing the endof its lifecycle and should be replaced. Therefore, the notification tothe consumer can include a notification that the garment should bereplaced.

Additionally, if the intended recipient of the notification is theconsumer that purchased the garment being managed then the contents ofthe notification can include advertisements from the retailer and/ormanufacturer. These advertisements can include information (e.g., stocklevels, prices, promotions, coupons, rebates, etc.) about identicalgarments, similar garments, related garments, improved garments, and/ordifferent garments. In this manner, the comfort-based garment managementsystem described herein allows the consumer of a garment to manage hisgarment and his garment related purchasing decisions.

If the intended recipient of the notification is a retailer (e.g., thatsold that garment, that now sells identical garments, the now sellssimilar garments, that now sells, related garments, that now sellsimproved garments, etc.) then the contents of the notification caninclude retailer-relevant information related to the garment. Forexample, the notification can include a notification that the garmentbeing managed is in need of replacement and/or will soon need to bereplaced. Such a notification can include an identification of thegarment being managed and/or identification (e.g., name, telephonenumber, email address, fax, location, address, zip code, age, sex,dimensions, race, etc.) of the consumer of the garment being managed.The notification can include a notification that the identified consumershould be included in directed advertising and mailings and might beinterested in particular offerings of the retailer. The notification canalso include a notification whether the retailer has a garment (e.g., anidentical garment, a similar garment, a related garment, etc.) in stockat the moment. If the retailer does not have the garment in stock, thenotification can include a suggestion of garments (e.g., type, size,color, etc.) that the retailer should bring in to stock. Additionally,if the retailer has an appropriate garment to replace the worn outgarment being managed, but the in-stock garment requires modification(e.g., tailoring) to meet the customers specifications (e.g., to createa fit that would produce sensor output data from a number of sensorsintegrated with the garment that will coincide with the customersdefinition of a comfortable fit), then the notification can includeinstructions as to the necessary modifications to the in-stock item.

If the intended recipient of the notification is a manufacturer thatmanufactured the garment being managed, then the contents of thenotification can include manufacturer-relevant information related tothe garment. For example, the notification can include the sensor outputdata of an identified garment for quality control and garmentimprovement uses. The notification can also include identification ofthe consumer of the garment being managed and/or identification of anassociated retailer (e.g., a retailer where the consumer purchased thegarment, retailers near the consumer's location, retailers carryingidentical and/or similar garments, etc.) and its current stock levels ofa garment (e.g., identical garment, similar garment, a related garment,etc.). Additionally, the notification can include a notification thatthe manufacturer needs to ship stock of a garment (e.g., identicalgarment, similar garment, a related garment, etc.) to a retailerassociated with the consumer.

FIG. 3 illustrates a diagram of an example of a computing device 350according to the present disclosure. The computing device 350 canutilize software, hardware, firmware, and/or logic to perform a numberof functions described herein. The computing device 350 can be anycombination of hardware and program instructions to share information.The hardware, for example, can include a processing resource 352 and/ora memory resource 354 (e.g., computer-readable medium (CRM), machinereadable medium (MRM), database, etc.). A processing resource 352, asused herein, can include any number of processors capable of executinginstructions stored by a memory resource 354. The processing resource352 may be implemented in a single device or distributed across multipledevices. The program instructions (e.g., computer readable instructions(CRI)) can include instructions stored on the memory resource 354 andexecutable by the processing resource 352 to implement a desiredfunction (e.g., establishing a sensor output threshold for a garmentbased on comfort data, establishing retail metric thresholds for thegarment, receiving sensor output data from a number of sensorsintegrated with a garment, receiving retail metrics for the garment,comparing the sensor output data with the sensor output threshold,comparing the retail metrics for the garment with the retail metricthresholds, identifying a suspected defective garment based on thesensor output threshold comparison and the retail metric thresholdcomparison, transmitting a number of notifications to a number ofparties associated with the garment in response to the identification,etc.).

The memory resource 354 can be in communication with a processingresource 352. A memory resource 354, as used herein, can include anynumber of memory components capable of storing instructions that can beexecuted by the processing resource 352. The memory resource 354 can bea non-transitory CRM or MRM. The memory resource 354 may be integratedin a single device or distributed across multiple devices. Further, thememory resource 354 may be fully or partially integrated in the samedevice as the processing resource 352 or it may be separate butaccessible to that device and the processing resource 352. Thus, it isnoted that the computing device 350 may be implemented on a participantdevice (e.g., host), on a server device, on a collection of serverdevices, and/or a combination of the participant device and the serverdevice.

The memory resource 354 can be in communication with the processingresource 352 via a communication link (e.g., a path) 356. Thecommunication link 356 can be local or remote to a machine (e.g., acomputing device) associated with the processing resource 352. Examplesof a local communication link 356 can include an electronic bus internalto a machine (e.g., a computing device) where the memory resource 354 isone of volatile, non-volatile, fixed, and/or removable storage medium incommunication with the processing resource 352 via the electronic bus.

A number of modules 358, 360, 362, 364, 366 can include CRI that whenexecuted by the processing resource 352 can perform a number offunctions. The number of modules 358, 360, 362, 364, 366 can besub-modules of other modules. For example, the receiving module 360 andthe comparing module 362 can be sub-modules and/or contained within thesame computing device. In another example, the number of modules 358,360, 362, 364, 366 can comprise individual modules at separate anddistinct locations (e.g., CRM, etc.).

Each of the number of modules 358, 360, 362, 364, 366 can includeinstructions that when executed by the processing resource 352 canfunction as a corresponding engine, including those as described herein.For example, the establishing module 358 can include instructions thatwhen executed by the processing resource 352 can function as thebaseline engine 236, the receiving module 360 can include instructionsthat when executed by the processing resource 352 can function as thecomfort engine 238, the comparing module 362 can include instructionsthat when executed by the processing resource 352 can function as thecomparison engine 240; and the transmitting module 366 can includeinstructions that when executed by the processing resource 352 canfunction as the notification engine 242.

The establishing module 358 can include CRI that when executed by theprocessing resource 352 can establish a sensor output threshold for agarment being managed based on comfort data. As described above, thecomfort data can be consumer dimensions, expected sensor output values,and/or baseline sensor output values. The thresholds, as describedabove, can be based on any of the comfort data and can define sensoroutput values corresponding to maximum and/or minimum sensor outputvalues of a number of sensors integrated with the garment being managedcoinciding with a comfortable fit of the garment being managed for theconsumer of the garment. Additionally, the establishing module 358 caninclude CRI that when executed by the processing resource 352 canestablish retail metric thresholds for the garment being managed. Retailmetrics can include any retail-related key performance indicators. Forexample, a retail metric of the garment being managed can include salesof identical garments, sales of similar garments, sales of relatedgarments, the average age of inventory of identical garments, theaverage age of inventory of similar garments, the average age ofinventory of related garments, the age of inventory for the garmentbeing managed, the number of times that the garment being managed hasbeen tried on, the number of times that the garment being managed hasbeen identified as uncomfortable by a consumer trying on the garmentbeing managed, etc. . . . .

Establishing a retail metric threshold can include establishing retailmetric values that, once exceeded, suggest that the garment beingmanaged may be defective. For example, establishing a retail metricthreshold can include establishing that the average amount of times thata garment (identical or similar to the garment being managed) is triedon be a number of consumers before being purchased is three times.Therefore, once the garment being managed has been tried on more thanthree times it can suggest that the garment is defective. Accordingly, aretail metric threshold of a garment being tried on three times can beestablished. Additionally, establishing a retail metric threshold caninclude establishing a retail metric threshold based on a combination ofretail metrics. For example, establishing a retail metric threshold fora garment being managed can include establishing that the average age ofinventory for identical and/or similar garments is three months at aretailer and the average amount of times that an identical and/orsimilar garment is tried on before being purchased is three. Therefore,once the age of inventory for the garment being managed exceeds threemonths and the garment being managed has been tried on more than threetimes it can suggest that the garment is defective. Accordingly, aretail metric threshold of three months age of inventory and three tryon sessions for the garment being managed can be established.

The retail metric threshold for a garment being managed can beestablished based on projections, industry standards, historical retaildata and/or other retail information related to the garment beingmanaged. For example, retailer and/or manufacturer records can beanalyzed to determine expected retail metric values for the garmentbeing managed and the thresholds can be established based on the retailmetric values.

The receiving module 360 can include CRI that when executed by theprocessing resource 352 can receive sensor output data from a number ofsensors integrated with a garment being managed. As described above, thesensor output data can be collected continuously or periodically.Receiving the sensor output data can include receiving the sensor outputdata in the cloud. Additionally, the receiving module 360 can includeCRI that when executed by the processing resource 352 can receive retailmetrics for the garment. The retail metrics can be received from themanufacturer and/or the retailer. For example, the retail metrics can bereceived in real-time from the retailer to provide an accurate currentpicture of the condition of the retailer's garment stock. The receivedretail metrics can include retail metrics for the retailer's entiregarment stock or retail metrics specific to the garment being managed.

The comparing module 362 can include CRI that when executed by theprocessing resource 352 can compare the sensor output data of the numberof sensors integrated with the garment being managed with the sensoroutput threshold and compare the retail metrics for the garment beingmanaged with the retail metric thresholds.

The identifying module 364 can include CRI that when executed by theprocessing resource 352 can identify a suspected defective garment beingmanaged based on the sensor output threshold comparison and the retailmetric threshold comparison. Identifying a suspected defective garmentcan include identifying a suspected defective garment based on thesensor output data of the garment being managed crossing a sensor outputthreshold and/or the retail metrics for the garment being managedcrossing the retail metric thresholds for the garment. For example, agarment being managed can be identified as a suspected defective garmentbased on the sensor output data of the garment indicating pressuremeasurements that exceed a threshold pressure measurement coincidingwith a comfortable fit for the consumer of the garment being managed andthe retail metrics for the garment being managed indicating that thegarment has been tried on ten times and has an inventory age of fivemonths. Identifying a suspected garment can include assigning a defectscore to the garment. The defect score can increase with each instanceof the sensor output data of the number of sensors integrated with thegarment being managed crossing the sensor output threshold and/or theretail metrics of the garment being managed crossing the retail metricsthresholds. The defect score can also increase with increased magnitudesof violation of the sensor output threshold and/or retail metricthreshold by the number of sensors of the garment and the retail metricsof the garment, respectively.

The transmitting module 366 can include CRI that when executed by theprocessing resource 352 can transmit a number of notifications to anumber of parties associated with the garment being managed in responseto the identification of the garment being managed as a suspecteddefective garment. The parties associated with the garment can, forexample, include a consumer of the garment, a retailer of the garment,and/or a manufacturer of the garment.

The number of notifications can include a notification identifying thesuspected defective garment. For example, the notification can include anotification to a consumer, retailer, and/or manufacturer of the garmentspecifying the identity (e.g., a description, an ID number associatedphysically and/or electronically with the garment, a stock keeping unit(SKU), any other information capable of identifying the particulargarment, etc.) of the garment and notifying the party that the garmentis suspected to be defective. Such a notification can includesuggestions on how to address the defective garment being managed. Forexample, the suggestions can include instructions to inspect thegarment, perform suggested remedial measures on the garment, send thegarment for repair, send the garment for replacement, request new stockof the garment, and/or send new stock of the garment. For example, thenotification can include a notification to the retailer identifying thesuspected defective garment and a notification to the manufacturer toreplace the suspected garment at the retailer with a non-defectivegarment.

Additionally, the number of notifications can include suggestions of aconsumer and/or a retailer associated with a consumer that may match thesuspected defective garment. For example, the notification can be anotification to a retailer identifying a specific consumer that might beinterested in purchasing the suspected defective garment that theretailer has in stock. When the sensor output data from the number ofsensors integrated with the suspected defective garment suggested aspecific defect (e.g., a specific irregular dimension), the notificationcan, for example, include a particular consumer with consumer dimensionsthat match the irregular dimensions of the suspected defective garment.The suggestion can be based on analysis of the sensor output data fromthe number of sensors integrated with the suspected defective garment,retail metrics of the suspected defective garments, a consumerinformation database of the manufacturer, retailer, and/or consumer,etc. . . . .

FIG. 4 is a flow chart of an example of a method 470 for comfort-basedgarment management according to the present disclosure. Method 470 canbe performed by a computing device (e.g., computing device 350,previously described in connection with FIG. 3), for instance.

At 472 the method 470 can include establishing a sensor output thresholdfor a garment being managed based on comfort data. As described above,comfort data can include any data that can indicate sensor output valuesfor a garment that would coincide with a comfortable fit for theconsumer of the garment being managed. For example, comfort data caninclude consumer dimensions, manufacturer specifications, expectedsensor output values, and/or baseline sensor output values. Establishinga sensor output threshold based on this comfort data can includeestablishing a minimum and/or maximum sensor output value from thenumber of sensors integrated with a garment being managed that wouldstill coincide with a comfortable fit for the consumer.

At 474 the method 470 can include analyzing retail data for a garment.Analyzing retail data can include receiving and analyzing retail metricsfor a garment. For example, analyzing retail data can include receivingand analyzing retailed-related metrics related to the garment beingmanaged (e.g., sales of identical garments, sales of similar garments,sales of related garments, the average age of inventory of identicalgarments, the average age of inventory of similar garments, the averageage of inventory of related garments, the age of inventory for thegarment being managed, the number of times that the garment beingmanaged has been tried on, the number of times that the garment beingmanaged has been identified as uncomfortable by a consumer trying on thegarment being managed, etc.).

In some instances, the retail data can include historical sensor outputdata from a number of garments associated with a number of consumersthat purchased the number of garments at a common retailer. For example,the retail data for garment store X can include the historical sensoroutput data of all or some of the number of consumers that havepurchased a particular garment at store X or its affiliates. This bodyof historical sensor output data not only can serve as a repository ofconsumer specific garment sensor output values, but also can be utilizedto generate a customer profile for the average customer shopping atgarment store X, in this example. The customer profile can be used todetermine the garment comfort preferences and garment needs of a groupof consumers that shop at a particular retailer. This information can beuseful, for example, for a manufacturer to determine what types ofgarments to design and/or ship to a particular retailer. Additionally,the customer profiles can allow the manufacturer to developretailer-specific specifications for its garments. For example, if thecustomer profile for consumers shopping at a garment store X indicatesthat the customers of this retailer prefer to wear, as an example, theirjeans much tighter (e.g., as indicated by sensor output data showinghigher pressure readings in garment sensors throughout the legs of thejeans) than consumers at other retailers, then the manufacturer can sendtighter fitting jeans to store X. Additionally, the manufacturer canadjust the expected pressure sensor values of the number of pressuresensors integrated into the legs of the jeans and these adjustedexpected values can be used in determining adjusted sensor output datathresholds for garments sold at store X.

At 476 the method 470 can include receiving sensor output data from anumber of sensors on a garment being managed. The sensor output data canbe received periodically or continuously. Once the sensor output datafrom a number of sensors of a garment being managed is received, thesensor output data can be added to the historical sensor output data ofthe retailer from which the garment being managed is purchased. That is,the historical sensor output data portion of the retail data can beupdated to include the newly received sensor output data of the garmentbeing managed.

At 478 the method 470 can include comparing the sensor output data withthe sensor output threshold. This comparison can be done with every newsensor output value received from the number of sensors integrated withthe garment being managed, periodically on some new sensor output valuesreceived from the number of sensors integrated with the garment beingmanaged, or it can be done on batches of sensor output values receivethe number of sensors integrated with the garment being managed.

At 480 the method 470 can include sending a notification to a partyassociated with the garment in response to analysis of the retail dataand the sensor output data of the garment being managed crossing thesensor output threshold. The party associated with the garment can, forexample, be the consumer that purchased the garment being managed, theretailer who sells the garment being managed, and/or the manufacturer ofthe garment being managed. The notification can be a notificationrelated to the garment being manufactured. For example the notificationcan be a notification to the consumer that purchased the garment beingmanaged indicating that the garment is reaching the end of a pre-definedlifecycle as described above. The notification can include, in anotherexample, a notification to the retailer of the garment being managedthat a particular consumer is in need of a new garment. Such anotification can identify the particular consumer. In another example,the notification can be a notification to a manufacturer and/or aretailer including the updated historical sensor output data.

The specification examples provide a description of the applications anduse of the system and method of the present disclosure. Since manyexamples can be made without departing from the spirit and scope of thesystem and method of the present disclosure, this specification setsforth some of the many possible example configurations andimplementations.

What is claimed:
 1. A system for comfort-based garment management,comprising: a baseline engine to establish a sensor output threshold fora garment based on comfort data, wherein the sensor output threshold isbased at least in part on baseline sensor output values that correspondwith a comfortable garment fit as identified by a consumer of thegarment and wherein the baseline sensor output values are associatedwith one or more dimensions of the consumer; a comfort engine to receivesensor output data from a number of sensors integrated with the garment;a comparison engine to compare the sensor output data with the sensoroutput threshold; and a notification engine to send a notification to aparty associated with the garment in response to the sensor output datacrossing the sensor output threshold.
 2. The system of claim 1,including the baseline engine to establish the sensor output thresholdfor the garment based on comfort data, wherein the baseline sensoroutput values are collected while the consumer is wearing the garment.3. The system of claim 1, including the baseline engine to establish thesensor output threshold for the garment based on comfort data, whereinthe sensor output threshold is based on a manufacturer's specificationfor the garment.
 4. The system of claim 1, including the notificationengine to send the notification to the party associated with the garmentin response to the sensor output data crossing the sensor outputthreshold, wherein the notification includes a notification that thegarment is reaching the end of a pre-defined lifecycle.
 5. A method forcomfort-based garment management, comprising: establishing a sensoroutput threshold for a garment based on comfort data, wherein the sensoroutput threshold is based at least in part on baseline sensor outputvalues that correspond with a comfortable garment fit as identified by aconsumer of the garment and wherein the baseline sensor output valuesare associated with one or more dimensions of the consumer; receivingsensor output data from a number of sensors integrated with the garment;comparing the sensor output data with the sensor output threshold; andsending a notification to a party associated with the garment inresponse to analysis of the retail data and the sensor output datacrossing the sensor output threshold.
 6. The method of claim 5, whereinthe method includes analyzing the retail data for the garment, whereinthe retail data includes an age of inventory for the garment at aretailer.
 7. The method of claim 5, wherein the method includesanalyzing the retail data for the garment, wherein the retail dataincludes historical sensor output data from a number of garmentsassociated with a number of consumers that purchased the number ofgarments at a common retailer.
 8. The method of claim 7, furthercomprising: updating the historical sensor output data to include thereceived sensor output data from the number of sensors integrated withthe garment if the garment was purchased at the common retailer, andincluding the updated historical sensor output data in the notification.9. The method of claim 5, wherein the method includes sending thenotification to the party associated with the garment in response toanalysis of the retail data and the sensor output data crossing thesensor output threshold, wherein the party associated with the garmentis a consumer of the garment.
 10. The method of claim 5, wherein themethod includes sending the notification to the party associated withthe garment in response to analysis of the retail data and the sensoroutput data crossing the sensor output threshold, wherein the partyassociated with the garment in a retailer of the garment and thenotification includes the identification of a consumer of the garment.11. The method of claim 5, wherein the method includes receiving sensoroutput data from a number of sensors integrated with a garment, whereinthe number of sensors integrated with the garment include at least oneof a heat sensor, a pressure sensor, an accelerometer, a gyroscope, atemperature sensor, a footfall sensor, a flex sensor, a thicknesssensor, a chemical sensor, a tensile load sensor, a compressive loadsensor, a light sensor, and a perspiration sensor.
 12. A non-transitorycomputer readable medium storing instructions executable by a processingresource to cause a computer to: establish a sensor output threshold fora garment based on comfort data, wherein the sensor output threshold isbased at least in part on baseline sensor output values that correspondwith a comfortable garment fit as identified by a consumer of thegarment and wherein the baseline sensor output values are associatedwith one or more dimensions of the consumer; receive sensor output datafrom a number of sensors on a garment; compare the sensor output datawith the sensor output threshold; and transmit a number of notificationsto a number of parties associated with the garment in response to theidentification.
 13. The medium of claim 12, wherein to transmit thenumber of notifications to the number of parties associated with thegarment in response to the identification includes: to transmit anotification identifying the suspected defective garment to a retailerand including suggestions on addressing the suspected defective garment.14. The medium of claim 12, wherein to transmit the number ofnotifications to the number of parties associated with the garment inresponse to the identification includes: to transmit a notification amanufacturer to replace the suspected defective garment with anon-defective garment at a retailer where the suspected defectivegarment is located.